HLT data compression vs event rejection

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HLT -data compression vs event rejection
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Assumptions

• Need for an online rudimentary event
  reconstruction for monitoring
• Detector readout rate (i.e. TPC) gtgt DAQ
  bandwidth ? mass storage bandwidth
• Some physics observables require running
  detectors at maximum rate (e.g. quarkonium
  spectroscopy TPC/TRD
  dielectrons jets in pp TPC tracking)
• Online combination of different detectors can
  increase selectivity of triggers (e.g. jet
  quenching PHOS/TPC high-pT ? - jet events)

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Data volume and event rate
bandwidth
TPC detector data volume 300 Mbyte/event data
rate 200 Hz
60 Gbyte/sec
front-end electronics
15 Gbyte/sec
Level-3 system
lt 2 Gbyte/sec
DAQ event building
lt 1.2 Gbyte/sec
permanent storage system
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HLT tasks

• Online (sub)-event reconstruction
• optimization and monitoring of detector
  performance
• monitoring of trigger selectivity
• fast check of physics program
• Data rate reduction
• data volume reduction
• regions-of-interest and partial readout
• data compression
• event rate reduction
• (sub)-event reconstruction and event rejection
• pp program
• pile-up removal
• charged particle jet trigger, etc.

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Data rate reduction

• Volume reduction
• regions-of-interest and partial readout
• data compression
• entropy coder
• vector quantization
• TPC-data modeling
• Rate reduction
• (sub)-event reconstruction and event rejection
  before event building

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TPC event(only about 1 is shown)
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Regions-of-interest and partial readout

• Example selection of TPC sector and ?-slice
  based on TRD track candidate

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Data compressionEntropy coder
Probability distribution of 8-bit TPC data

• Variable Length Coding
• short codes for long codes for
• frequent values infrequent values
• Results
• NA49 compressed event size 72
• ALICE 65
• (Arne Wiebalck, diploma thesis, Heidelberg)

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Data compressionVector quantization

• Sequence of ADC-values on a pad vector

compare
code book

• Vector quantization transformation of
  vectors into codebook entries
• Quantization error

Results NA49 compressed event size 29
ALICE 48-64 (Arne Wiebalck, diploma
thesis, Heidelberg)
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Data compression TPC-data modeling

• Fast local pattern recognition
  

simple local track model (e.g. helix)
track parameters

• Track and cluster modeling

comparison to raw data
local track parameters
analytical cluster model
quantization of deviations from track and
cluster model
Result NA49 compressed event size 7
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Fast pattern recognition

• Essential part of Level-3 system
• crude complete event reconstruction
• ? monitoring
• redundant local tracklet finder for cluster
  evaluation ? efficient data compression
• selection of (?,?,pT)-slices
• ? ROI
• high precision tracking for selected track
  candidates
• jets, dielectrons, ...

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Fast pattern recognition

• Sequential approach
• cluster finder, vertex finder and track follower
• STAR code adapted to ALICE TPC
• reconstruction efficiency
• timing results
• Iterative feature extraction
• tracklet finder on raw data and cluster
  evaluation
• Hough transform

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Fast cluster finder (1)

• timing 5ms per padrow

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Fast cluster finder (2)
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Fast cluster finder (3)

• Efficiency

• Offline efficiency

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Fast vertex finder

• Resolution

• Timing result
• 19 msec on ALPHA (667 MHz)

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Fast track finder

• Tracking efficiency

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Fast track finder

• Timing results

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Hough transform (1)

• Data flow

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Hough transform (2)

• ?-slices

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Hough transform (3)

• Transformation and maxima search

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Level-3 system architecture
TPC sector 1
TPC sector 36
TRD
ITS
XYZ
ROI
local processing subsector/sector
data compr.
global processing I (2x18 sectors)
Level-3 trigger
momentum filter
global processing II (detector merging)
event rejection
global processing III (event reconstruction)
monitoring
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TPC on-line tracking

• Assumptions
• Bergen fast tracker
• DEC Alpha 667 MHz
• Fast cluster finder excluding cluster
  deconvolution
• Note This cluster finder is sub optimal for the
  inner sectors and additional work is required
  here. However in order to get some estimate the
  computation requirements were based on the outer
  pad rows. It should be noted that the possibly
  necessary deconvolution in the inner padrows may
  require comparably more CPU cycles.
• TPC L3 Tracking estimate
• Cluster finder on pad row of the outer
  sector 5 ms
• tracking of all (monte carlo) space points for
  one TPC sector 600 msNote - this data may not
  include realistic noise
• - tracking to first order is linear
  with the number of tracks provided there are few
  overlaps
• - assuming one ideal processor below
• Cluster finder on one sector (145 padrows)
  725 ms
• Process complete sector 1,325 s
• Process complete TPC 47,7 s
• Running at maximum TPC rate (200 Hz), January
  2000 9540 CPUs
• Assuming 20 overhead 11500 CPUs
  (parallel computation, network transfer, inner
  sector additional overhead, sector merging etc.)
• Moores Law (60/a) ? _at_ 2006 1a commission
  x10,5 1095 CPUs